NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge

Yow-Ting Shiue, Hen-Hsen Huang, Hsin-Hsi Chen


Abstract
This paper presents the NTU NLP Lab system for the SemEval-2018 Capturing Discriminative Attributes task. Word embeddings, pointwise mutual information (PMI), ConceptNet edges and shortest path lengths are utilized as input features to build binary classifiers to tell whether an attribute is discriminative for a pair of concepts. Our neural network model reaches about 73% F1 score on the test set and ranks the 3rd in the task. Though the attributes to deal with in this task are all visual, our models are not provided with any image data. The results indicate that visual information can be derived from textual data.
Anthology ID:
S18-1171
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
1027–1033
Language:
URL:
https://www.aclweb.org/anthology/S18-1171
DOI:
10.18653/v1/S18-1171
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1171.pdf